class_docstr: ARLSTem stemmer : a light Arabic Stemming algorithm without any dictionary.
  Department of Telecommunication & Information Processing. USTHB University,
  Algiers, Algeria.
  ARLSTem.stem(token) returns the Arabic stem for the input token.
  The ARLSTem Stemmer requires that all tokens are encoded using Unicode
  encoding.
class_docstr_tok: ["ARLSTem","stemmer",":","a","light","Arabic","Stemming","algorithm","without","any","dictionary",".","Department","of","Telecommunication","&","Information","Processing",".","USTHB","University",",","Algiers",",","Algeria",".","ARLSTem.stem","(","token",")","returns","the","Arabic","stem","for","the","input","token",".","The","ARLSTem","Stemmer","requires","that","all","tokens","are","encoded","using","Unicode","encoding","."]
docstr: stem the verb prefixes and suffixes or both
docstr_tok: ["stem","the","verb","prefixes","and","suffixes","or","both"]
code: def verb(self, token):
      """
      stem the verb prefixes and suffixes or both
      """
      vb = self.verb_t1(token)
      if vb is not None:
          return vb
      vb = self.verb_t2(token)
      if vb is not None:
          return vb
      vb = self.verb_t3(token)
      if vb is not None:
          return vb
      vb = self.verb_t4(token)
      if vb is not None:
          return vb
      vb = self.verb_t5(token)
      if vb is not None:
          return vb
      return self.verb_t6(token)
  
file_path: nltk/nltk/stem/arlstem.py
imports: import re
  from nltk.stem.api import StemmerI
label_desc: Use image node_id 8 for calling the ARLSTem obj's underlying member method code with example usage: obj.verb(token) and returns: self, vb, vb, vb, vb, vb